TY - JOUR
T1 - Optimization FOU of interval type-2 fuzzy inference system using big bang – big crunch algorithm for short term load forecasting on national holiday case study
T2 - South and central Kalimantan-Indonesia
AU - Ramadhani, Akhmad
AU - Dharma, Agus
AU - Robandi, Imam
N1 - Publisher Copyright:
© 2015, Praise Worthy Prize S.r.l. - All rights reserved.
PY - 2015/2/1
Y1 - 2015/2/1
N2 - The application interval type-2 fuzzy inference systems (IT2FIS) has been attention for a short-term load forecasting problems solution. This paper present optimization membership function of antecedent (X,Y) and consequent (Z) interval type-2 Fuzzy Logic System using Big Bang – Big Crunch Algorithm for application short term load forecasting on national holiday. This method has implemented on the historical peak load data during 14 national holiday case study in South and Central Kalimantan – Indonesia electrical power system on year 2008. The Big Bang–Big Crunch (BB–BC) algorithm will applied to optimization interval footprint of uncertainty (FOU) membership functions of interval type-2 fuzzy logic system. BB-BC Algorithm is a global optimization algorithm and has a low computational cost and a high convergence speed, so very efficient in this research. The test result showed main absolute percentage error (MAPE) less than interval type-2 fuzzy inference system (FIS) and type-1 fuzzy inference system (FIS). Finally, this paper defined main absolute percentage error (MAPE) 0.58045% for type-1 FIS, 0.53906% for interval type-2 FIS, and 0.52421% for optimization interval type-2 FIS-Big Bang Big Crunch Algorithm.
AB - The application interval type-2 fuzzy inference systems (IT2FIS) has been attention for a short-term load forecasting problems solution. This paper present optimization membership function of antecedent (X,Y) and consequent (Z) interval type-2 Fuzzy Logic System using Big Bang – Big Crunch Algorithm for application short term load forecasting on national holiday. This method has implemented on the historical peak load data during 14 national holiday case study in South and Central Kalimantan – Indonesia electrical power system on year 2008. The Big Bang–Big Crunch (BB–BC) algorithm will applied to optimization interval footprint of uncertainty (FOU) membership functions of interval type-2 fuzzy logic system. BB-BC Algorithm is a global optimization algorithm and has a low computational cost and a high convergence speed, so very efficient in this research. The test result showed main absolute percentage error (MAPE) less than interval type-2 fuzzy inference system (FIS) and type-1 fuzzy inference system (FIS). Finally, this paper defined main absolute percentage error (MAPE) 0.58045% for type-1 FIS, 0.53906% for interval type-2 FIS, and 0.52421% for optimization interval type-2 FIS-Big Bang Big Crunch Algorithm.
KW - Big bang - big crunch algorithm
KW - Interval Type-2 fuzzy inference system
KW - MAPE
KW - STLF
UR - http://www.scopus.com/inward/record.url?scp=84937880399&partnerID=8YFLogxK
U2 - 10.15866/iree.v10i1.4871
DO - 10.15866/iree.v10i1.4871
M3 - Article
AN - SCOPUS:84937880399
SN - 1827-6660
VL - 10
SP - 123
EP - 130
JO - International Review of Electrical Engineering
JF - International Review of Electrical Engineering
IS - 1
ER -